Angular-Based 3D Hybrid Precoding for URA in Multi-User Massive MIMO Systems
Bibliographic record
Abstract
This paper proposes a new angular-based 3D two- stage hybrid precoding scheme for multi-user massive MIMO systems using uniform rectangular arrays (URA), where users are partitioned into different groups based on the similarity of their angle-of-departure (AoD) information. At first using the user-group AoD ranges, the RF- beamforming stage is designed to reduce the inter- group interference, the number of RF chains, and the channel state information (CSI) overhead. Then, the digital baseband precoder stage is constructed via regularized zero-forcing (RZF) technique using the effective channel seen from baseband to reduce the intra-group interference between the users, considering three approaches: joint-group-processing (JGP), per-group-processing (PGP) and common-group-processing (CGP). Illustrative results indicate that the proposed two-stage hybrid precoding schemes with the reduced hardware cost/complexity and relaxed CSI estimation overhead can closely approach the sum- rate performance of the ideal single-stage fully- digital precoding. Moreover, their performance gap becomes negligible as the array size increases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".